The present exemplary embodiments relate to automated diagnosis and production in systems having multiple resources for achieving production goals. In such systems, automated diagnosis of system performance and component status can advantageously aid in improving productivity, identifying faulty or underperforming resources, scheduling repair or maintenance, etc. Accurate diagnostics requires information about the true condition of components in the production system. Such information can be obtained directly from sensors associated with individual components and/or may be inferred from a limited number of sensor readings within the production plant using a model or other knowledge of the system structure and dynamics. Providing complete sensor coverage for all possible system faults can be expensive or impractical in harsh production environments, and thus it is generally preferable to instead employ diagnostic procedures to infer the source of faults detected or suspected from limited sensors. System diagnostic information is typically gathered by one of two methods, including dedicated or explicit diagnostics with the system being exercised while holding production to perform tests and record observations without attaining any production, as well as passive diagnostics in which information is gathered from the system sensors during normal production. Although the latter technique allows inference of some information without disrupting production, the regular production mode may not sufficiently exercise the system to provide adequate diagnostic information to improve long term productivity. Moreover, while dedicated diagnostic operation generally provides better information than passive diagnostics, the cost of this information is high in terms of short term productivity reduction, particularly when diagnosing recurring intermittent system component failures that require repeated diagnostic interventions. Conventional production system diagnostics are thus largely unable to adequately yield useful diagnostic information without halting production and incurring the associated costs of system down-time, and are therefore of limited utility in achieving long term system productivity. Accordingly, a need remains for improved control systems and techniques by which both long term and short term productivity goals can be achieved in production systems having only limited sensor deployment.